Objective To introduce a modified technique of right ventricular outflow tract (RVOT) reconstruction using a handmade bicuspid pulmonary valve crafted from expanded polytetrafluoroethylene (ePTFE) and to summarize the early single-center experience. Methods Patients with complex congenital heart diseases (CHD) who underwent RVOT reconstruction with a handmade ePTFE bicuspid pulmonary valve due to pulmonary regurgitation at Guangdong Provincial People’s Hospital from April 2021 to February 2022 were selected. Postoperative artificial valve function and right heart function indicators were evaluated. Results A total of 17 patients were included, comprising 10 males and 7 females, with a mean age of (18.18±12.14) years and a mean body weight of (40.94±19.45) kg. Sixteen patients underwent reconstruction with a handmade valved conduit, with conduit sizes ranging from 18 to 24 mm. No patients required mechanical circulatory support, and no in-hospital deaths occurred. During a mean follow-up period of 12.89 months, only one patient developed valve dysfunction, and no related complications or adverse events were observed. The degree of pulmonary regurgitation was significantly improved post-RVOT reconstruction and during follow-up compared to preoperative levels (P<0.001). Postoperative right atrial diameter, right ventricular diameter, and tricuspid regurgitation area were all significantly reduced compared to preoperative values (P<0.05). Conclusion The use of a 0.1 mm ePTFE handmade bicuspid pulmonary valve for RVOT reconstruction in complex CHD is a feasible, effective, and safe technique.

BACKGROUND:Flap transplantation technique is a commonly used surgical procedure for the treatment of severe tissue defects,but postoperative flap necrosis is easily triggered by ischemia-reperfusion injury.Therefore,it is still an important research topic to improve the survival rate of transplanted flaps. OBJECTIVE:To review the pathogenesis and latest treatment progress of flap ischemia-reperfusion injury. METHODS:CNKI,WanFang Database and PubMed database were searched for relevant literature published from 2014 to 2024.The search terms used were"flap,ischemia-reperfusion injury,inflammatory response,oxidative stress,Ca2+overload,apoptosis,mesenchymal stem cells,platelet-rich plasma,signaling pathways,shock wave,pretreatment"in Chinese and English.After elimination of irrelevant literature,poor quality and obsolete literature,77 documents were finally included for review. RESULTS AND CONCLUSION:Flap ischemia/reperfusion injury may be related to pathological factors such as inflammatory response,oxidative stress response,Ca2+overload,and apoptosis,which can cause apoptosis of vascular endothelial cells,vascular damage and microcirculation disorders in the flap,and eventually lead to flap necrosis.Studies have found that mesenchymal stem cell transplantation,platelet-rich plasma,signaling pathway modulators,shock waves,and pretreatment can alleviate flap ischemia/reperfusion injuries from different aspects and to varying degrees,and reduce the necrosis rate and necrosis area of the grafted flap.Although there are many therapeutic methods for skin flap ischemia/reperfusion injury,a unified and effective therapeutic method has not yet been developed in the clinic,and the advantages and disadvantages of various therapeutic methods have not yet been compared.Most of the studies remain in the stage of animal experiments,rarely involving clinical observations.Therefore,a lot of research is required in the future to gradually move from animal experiments to the clinic in order to better serve the clinic.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation, joint destruction, and functional impairment. Angiogenesis plays a key role in the pathological progression of RA with dysfunction of endothelial cells to promote synovial inflammation, sustain pannus formation, subsequently leading to joint damage. Colquhounia Root Tablets (CRT), a Chinese patent drug, has shown a satisfying clinical efficacy in treating RA, while the underlying mechanism by which CRT inhibits RA-associated angiogenesis remains unclear. In this study, we applied a research approach combining transcriptomic data analysis, bio-network mapping, and in vivo and in vitro experiments to explore the molecular mechanisms of CRT in suppressing angiogenesis in RA. Animal welfare and experimental procedures follow the regulations of the Animal Ethics Committee of Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences (ratification number: IBTCMCACMS21-2307-06). Network analysis identified that key genes such as nucleotide-binding oligomerization domain-containing protein 2 (NOD2), SMAD family member 3 (SMAD3), and vascular endothelial growth factor A (VEGFA) significantly enriched in pathways related to NOD-like receptor signaling and VEGF signaling, indicating that CRT may inhibit angiogenesis by regulating vascular endothelial cell function with modulating angiogenesis-related pathways. In vivo data showed that CRT significantly reduced the positive expression of CD31 and VEGF in the ankle joint of adjuvant-induced arthritis (AIA) rats. In vitro data further confirmed that CRT effectively inhibited VEGF-induced migration, invasion, and tube formation in HUVECs, while significantly reduced the expression of angiogenesis-related factors VEGF/CD31/Ang-1, as well as the positive expression of VEGF and CD31 in HUVECs. Furthermore, CRT markedly decreased the protein expression of NOD2, VEGFA, and SMAD3. In conclusion, these findings indicate that CRT may inhibit the RA-related angiogenesis by targeting the NOD2/SMAD3/VEGF signaling axis to improve endothelial cell function, enriching the scientific connotation of CRT in inhibiting pathological angiogenesis in RA and also offer new insights for clinical prevention and treatment of RA.

Objective: To explore the comorbidity and associated factors of dental caries and overweight/obesity among primary and secondary school students in Guangxi, so as to provide a scientific basis for the development of targeted prevention strategies. Methods: A stratified cluster random sampling method was used to survey 178 700 students from the fourth grade of primary school to the third year of high school in Guangxi Zhuang Autonomous Region from September to November 2023, including physical examination, oral screening, and questionnaire survey. Chisquare tests and binary Logistic regression analysis were employed to investigate the related factors of the cooccurrence of dental caries and overweight/obesity among students. Results: The comorbidity rate of dental caries and overweight/obesity was 9.55%, with urban areas (9.95%) higher than rural counties (9.24%), boys (10.54%) higher than girls (8.54%), primary school students (11.49%) higher than senior high school students (8.92%) and junior high school students (8.05%), and nonboarding students (11.44%) higher than boarding students (7.94%), and all differences were statistically significant (χ2=26.07, 207.91, 471.54, 629.14,P<0.01). Multivariate Logistic regression analysis showed that consuming cereal for breakfast (OR=0.91, 95%CI=0.88-0.94), drinking milk in the past week (OR=0.89, 95%CI=0.83-0.95), meeting sleep standards (OR=0.95, 95%CI=0.91-0.99), and brushing teeth at least once a day (OR=0.82, 95%CI=0.73-0.93) had a lower risk of the comorbidity of dental caries and overweight/obesity. In contrast, drinking beverages in the past week (OR=1.14, 95%CI=1.09-1.20), consuming fried foods in the past week (OR=1.11, 95%CI=1.06-1.17), eating fruit ≥1 time every day (OR=1.06, 95%CI=1.02-1.11), consuming fruit ≥1 type every day (OR=1.07, 95%CI=1.01-1.12), and having fish, poultry, meat, or eggbased breakfasts (OR=1.03, 95%CI=1.05-1.13) had a higher risk of the comorbidity of dental caries and overweight/obesity (P<0.05). Conclusions Dietary habits and lifestyle behaviors are associated with the comorbidity of dental caries and overweight/obesity among primary and secondary school students in Guangxi. Guiding students to form healthy living habits is helpful to preven dental caries and overweight/obesity.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.